139,392 research outputs found
Deep Learning For Sequential Pattern Recognition
Projecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München
(TUM)In recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with the conventional algorithms for optimization and training make them efficient for variety of applications in signal processing and pattern recognition. This thesis explores these novel techniques and their related algorithms. It addresses and compares different attributes of these methods, sketches in their possible advantages and disadvantages
When Can You Fold a Map?
We explore the following problem: given a collection of creases on a piece of
paper, each assigned a folding direction of mountain or valley, is there a flat
folding by a sequence of simple folds? There are several models of simple
folds; the simplest one-layer simple fold rotates a portion of paper about a
crease in the paper by +-180 degrees. We first consider the analogous questions
in one dimension lower -- bending a segment into a flat object -- which lead to
interesting problems on strings. We develop efficient algorithms for the
recognition of simply foldable 1D crease patterns, and reconstruction of a
sequence of simple folds. Indeed, we prove that a 1D crease pattern is
flat-foldable by any means precisely if it is by a sequence of one-layer simple
folds.
Next we explore simple foldability in two dimensions, and find a surprising
contrast: ``map'' folding and variants are polynomial, but slight
generalizations are NP-complete. Specifically, we develop a linear-time
algorithm for deciding foldability of an orthogonal crease pattern on a
rectangular piece of paper, and prove that it is (weakly) NP-complete to decide
foldability of (1) an orthogonal crease pattern on a orthogonal piece of paper,
(2) a crease pattern of axis-parallel and diagonal (45-degree) creases on a
square piece of paper, and (3) crease patterns without a mountain/valley
assignment.Comment: 24 pages, 19 figures. Version 3 includes several improvements thanks
to referees, including formal definitions of simple folds, more figures,
table summarizing results, new open problems, and additional reference
Automatic Structural Scene Digitalization
In this paper, we present an automatic system for the analysis and labeling
of structural scenes, floor plan drawings in Computer-aided Design (CAD)
format. The proposed system applies a fusion strategy to detect and recognize
various components of CAD floor plans, such as walls, doors, windows and other
ambiguous assets. Technically, a general rule-based filter parsing method is
fist adopted to extract effective information from the original floor plan.
Then, an image-processing based recovery method is employed to correct
information extracted in the first step. Our proposed method is fully automatic
and real-time. Such analysis system provides high accuracy and is also
evaluated on a public website that, on average, archives more than ten
thousands effective uses per day and reaches a relatively high satisfaction
rate.Comment: paper submitted to PloS On
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